It is a software framework used for writing applications that process large amounts of data(Structured and Unstructured)

It is useful for batch processing on terabytes or petabytes of data stored in Apache Hadoop.

The Hadoop framework partitions the input dataset into independent chunks, and then, map task process a set of chunks in a parallel manner. It sorts the outputs of the maps, and then, gives the processed task as the input to the reduce tasks. File-System stores both the input and output of the job. The Hadoop framework is responsible for task scheduling, monitoring, and failed task re-execution.

PayLoad: PayLoad applications implement the two most vital functions such as the Map and Reduce functions

Mapper: It maps the input/value pair with the intermediate key/value pairs

NameNode: It is associated with HDFS

DataNode: It contains the data before the processing

MasterNode: It receives the job requests from the client, and JobTracker runs in Master node

SlaveNode: It runs both the Map and the Reduce tasks

JobTracker: allows job scheduling and reporting regarding the tracking of the jobs

TaskTracker: responsible for tracking the jobs and reporting about the job status to the JobTracker

Task: refers to the execution of the Mapper or the Reducer on a set of data

Simplicity

Scalability

Speed

Recovery

Minimal data motion